The solubility of Ti- and P-rich accessory minerals has been examined as a function of pressure and K2O/Na2O ratio in two series of highly evolved silicate systems. These systems correspond to (a) alkaline, varying from alkaline to peralkaline with increasing K2O/Na2O ratio; and (b) strongly metaluminous (essentially trondhjemitic at the lowest K2O/Na2O ratio) and remaining metaluminous with increasing K2O/Na2O ratio (to 3). The experiments were conducted at a fixed temperature of 1000 °C, with water contents varying from 5 wt.% at low pressure (0.5 GPa), increasing through 5–10 wt.% at 1.5–2.5 GPa to 10 wt.% at 3.5 GPa. Pressure was extended outside the normal crustal range, so that the results may also be applied to derivation of hydrous silicic melts from subducted oceanic crust.
For the alkaline composition series, the TiO2 content of the melt at Ti-rich mineral saturation decreases with increasing pressure but is unchanged with increasing K content (at fixed pressure). The P2O5 content of the alkaline melts at apatite saturation increases with increased pressure at 3.5 GPa only, but decreases with increasing K content (and peralkalinity). For the metaluminous composition series (termed as “trondhjemite-based series” (T series)), the TiO2 content of the melt at Ti-rich mineral saturation decreases with increasing pressure and with increasing K content (at fixed pressure). The P2O5 content of the T series melts at apatite saturation is unchanged with increasing pressure, but decreases with increasing K content. The contrasting results for P and Ti saturation levels, as a function of pressure in both compositions, point to contrasting behaviour of Ti and P in the structure of evolved silicate melts. Ti content at Ti-rich mineral saturation is lower in the alkaline compared with the T series at 0.5 GPa, but is similar at higher pressures, whereas P content at apatite saturation is lower in the T series at all pressures studied. The results have application to A-type granite suites that are alkaline to peralkaline, and to I-type metaluminous suites that frequently exhibit differing K2O/Na2O ratios from one suite to another. 相似文献
One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively. 相似文献
The U.S. Geological Survey has developed a technique that allows mineral resource experts to apply economic filters to estimates of undiscovered mineral resources. This technique builds on previous work that developed quantitative methods for mineral resource assessments. A Monte-Carlo calculation uses mineral deposit models to estimate commodity grades and tonnages of undiscovered deposits. The results then are analyzed using simple estimates of capital expenditures and daily operating costs for a mine and associated mill. The daily operating costs and the value of the ore are used to calculate the net present value of the deposit, which is compared to the capital expenditures to determine whether the deposit is economic. Repetition of these calculations for many deposits produces a table that can be interpreted in terms of the probability of there being deposits that have anet present value exceeding some specified amount. Sample calculations indicate that applying economic filters to simulated mineral resources might change the perception of the results compared to presenting the calculations in terms of the expected mean gross-in-place value of the minerals. 相似文献
Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach. 相似文献